By Hoa Khanh Dam, Jeremy Pitt, Yang Xu, Guido Governatori, Takayuki Ito
This booklet constitutes the refereed court cases of the seventeenth overseas convention on rules and perform of Multi-Agent structures, PRIMA 2014, held in Gold Coast, QLD, Australia, in December 2014.
The convention used to be co-located with the thirteenth Pacific RIM overseas convention on synthetic Intelligence, PRICAI 2014.
The 21 revised complete papers offered including 15 brief papers have been conscientiously reviewed and chosen from seventy seven submissions. The papers are prepared in topical sections on self association and social networks/crowdsourcing; common sense and argumentation; simulation and insurance; interplay and functions; norms, video games and social selection; and metrics, optimisation, negotiation and learning.
Read or Download PRIMA 2014: Principles and Practice of Multi-Agent Systems: 17th International Conference, Gold Coast, QLD Australia, December 1-5, 2014. Proceedings PDF
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Additional resources for PRIMA 2014: Principles and Practice of Multi-Agent Systems: 17th International Conference, Gold Coast, QLD Australia, December 1-5, 2014. Proceedings
This helps the Social Consumers to increase their satisfaction over the Selﬁsh Consumers. And at last, the Optimum allocator achieved the highest consumer satisfaction average, and since there is no better allocation distribution, no exchanges were performed. Although using a centralised allocation method (Optimum) shows better results, using Social Capital lightly under-performs and relives the systems from the scalability issues. Including more consumer ﬂexibility into the Optimum Allocation method will require a more complex algorithm.
Agent ax records all the resx→i ( t ) exceeding the threshold. Then, if ax has an IA acquaintance, ax submits the requirement to the IA and the IA will handle with the candidate selection problem. The agent ax needs to wait for the reply from IA and then connects another EA according to the recommendation. In contrast, if there are no IA acquaintances, EA should reorganize the structure by itself. Destroying a peer-to-peer relation is also significant as keeping a useless relation induces cost for both sides.
E. each agent will receive a diﬀerent set of results). We use it to model agents observation, communication with other agents or any form of publishing results that will deﬁciently spread the match results to other agents. Social agents update their social capital with this information (lines 5-7). Subsequently, they again update their social capital with all the information received by the institutions they are member of (lines 8-10). All institutions send information about who joined, left, was sanctioned, was rewarded or was expelled.